proj                  package:base                  R Documentation

_P_r_o_j_e_c_t_i_o_n_s _o_f _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     'proj' returns a matrix or list of matrices giving the projections
     of the data onto the terms of a linear model.  It is most
     frequently used for 'aov' models.

_U_s_a_g_e:

     proj(object, ...)  

     ## S3 method for class 'aov':
     proj(object, onedf = FALSE, unweighted.scale = FALSE, ...)  

     ## S3 method for class 'aovlist':
     proj(object, onedf = FALSE, unweighted.scale = FALSE, ...)  

     ## Default S3 method:
     proj(object, onedf = TRUE, ...)  

     ## S3 method for class 'lm':
     proj(object, onedf = FALSE, unweighted.scale = FALSE, ...)  

_A_r_g_u_m_e_n_t_s:

  object: An object of class '"lm"' or a class inheriting from it, or
          an object with a similar structure including in particular
          components 'qr' and 'effects'.

   onedf: A logical flag. If 'TRUE', a projection is returned for all
          the columns of the model matrix. If 'FALSE', the
          single-column projections are collapsed by terms of the model
          (as represented in the analysis of variance table).

unweighted.scale: If the fit producing 'object' used weights, this
          determines if the projections correspond to weighted or
          unweighted observations.

     ...: Swallow and ignore any other arguments.

_D_e_t_a_i_l_s:

     A projection is given for each stratum of the object, so for 'aov'
     models with an 'Error' term the result is a list of projections.

_V_a_l_u_e:

     A projection matrix or (for multi-stratum objects) a list of
     projection matrices.

     Each projection is a matrix with a row for each observations and
     either a column for each term ('onedf = FALSE') or for each
     coefficient ('onedf = TRUE'). Projection matrices from the default
     method have orthogonal columns representing the projection of the
     response onto the column space of the Q matrix from the QR
     decomposition.  The fitted values are the sum of the projections,
     and the sum of squares for each column is the reduction in sum of
     squares from fitting that column (after those to the left of it).

     The methods for 'lm' and 'aov' models add a column to the
     projection matrix giving the residuals (the projection of the data
     onto the orthogonal complement of the model space).

     Strictly, when 'onedf = FALSE' the result is not a projection, but
     the columns represent sums of projections onto the columns of the
     model matrix corresponding to that term. In this case the matrix
     does not depend on the coding used.

_A_u_t_h_o_r(_s):

     The design was inspired by the S function of the same name
     described in Chambers _et al._ (1992).

_R_e_f_e_r_e_n_c_e_s:

     Chambers, J. M., Freeny, A and Heiberger, R. M. (1992) _Analysis
     of variance; designed experiments._ Chapter 5 of _Statistical
     Models in S_ eds J. M. Chambers and T. J. Hastie, Wadsworth &
     Brooks/Cole.

_S_e_e _A_l_s_o:

     'aov', 'lm', 'model.tables'

_E_x_a_m_p_l_e_s:

     N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0)
     P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0)
     K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0)
     yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,
     55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0)

     npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P),
                       K=factor(K), yield=yield)
     npk.aov <- aov(yield ~ block + N*P*K, npk)
     proj(npk.aov)

     ## as a test, not particularly sensible
     options(contrasts=c("contr.helmert", "contr.treatment"))
     npk.aovE <- aov(yield ~  N*P*K + Error(block), npk)
     proj(npk.aovE)

